Tracking and prediction of the future location of missile targets is at the forefront of antiballistic missile defense. Existing methods, such as Interactive Multiple Model (IMM) use multiple hypotheses, and use pattern recognition schemes which compare kinematic information about the target, such as time history of altitude, ground path, and flight angle versus time, with a template. This prior art requires large numbers of templates, as for each target there are altitude, flight path, ground path. The templates are based on the target taking a nominal path, but the target motors may burn long or short, thereby not matching the templates. The enemy may change any of the parameters of the flight path, such as lofting or depressing the flight path, so as to avoid matching a template. A template technique, to be effective in view of these possible changes, would require a very large number of templates, including a large number for each type of target. When there are very large numbers of templates, there will likely be an overlap between the targets, so that it becomes difficult to distinguish between a given target that is lofted and another target type that is depressed. For example, a target may have an engine which runs “hot” or “cold,” and it may be directed along a flight path which is either depressed or lofted, thus requiring a very large number of templates to handle even one target. When the target has multiple stages, each stage may itself run hot or cold, and there are even more variations. The large number of variations makes the use of kinematic templates very complex. In addition, the large number of templates may result in overlap of the kinematic features among various different templates, thereby leading to indeterminate results. The potential inaccuracies are exacerbated by variations of the kinematics attributable to inaccurate time after lift-off (TALO).
Improved missile identification and tracking systems and methods are desired.